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A noisy multi-objective evolutionary optimization algorithm based on Elman neural network
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DOI：10.1016/j.asoc.2025.113842.png)
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Abstract
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• E-NSGA-II is an attempt to utilize Elman neural network to solve NMOPs. • E-NSGA-II uses self-adaptive modeling to decide if an Elman network needs building. • The hybrid selection solution strategy picks individuals to enhance diversity. • The noise-driven sampling adjusts sampling times to boost accuracy and reduce cost.
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